Lorenzen, Steven Robert (2023)
Railway Bridge Monitoring with Minimal Sensor Deployment: Virtual Sensing and Resonance Curve-Based Drive-by Monitoring.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00026426
Ph.D. Thesis, Primary publication, Publisher's Version
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Item Type: | Ph.D. Thesis | ||||
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Type of entry: | Primary publication | ||||
Title: | Railway Bridge Monitoring with Minimal Sensor Deployment: Virtual Sensing and Resonance Curve-Based Drive-by Monitoring | ||||
Language: | English | ||||
Referees: | Schneider, Prof. Dr. Jens ; Waldmann-Diederich, Prof. Dr. Danièle | ||||
Date: | 14 December 2023 | ||||
Place of Publication: | Darmstadt | ||||
Collation: | xxvii, 202 Seiten | ||||
Date of oral examination: | 28 November 2023 | ||||
DOI: | 10.26083/tuprints-00026426 | ||||
Abstract: | The global railway bridge infrastructure is aging and exposed to increasing traffic loads. In Germany, the average lifespan of a railway bridge is 122 years. During this time, railway vehicles have evolved significantly, leading to higher axle loads and speeds. The urgent need to extend the lifespan of these bridges has prompted an intensive search for efficient methods to assess existing bridge structures. One approach is vibration-based monitoring, which can be categorised into direct and indirect monitoring. In direct monitoring, sensors are installed directly upon the structure. Conversely, in indirect monitoring, a passing vehicle equipped with sensors is utilised, commonly referred to as drive-by monitoring. Direct monitoring of bridges is still rare, and when bridges are instrumented, it is typically with a sparse deployment of sensors. Hence, methods are needed to extrapolate the measured structural responses to unmeasured locations. These methods are summarized under the term virtual sensing. Existing research on virtual sensing for railway bridges reveals gaps in long-term studies, especially considering different environmental and operational conditions. Notably, 95 % of all railway bridges in Germany are short-spanned with spans less than 30 m, and there is a significant lack of research in this segment. A main focus of this dissertation was to address this research gap. The high effort in direct instrumentation currently does not allow for monitoring of the entire railway bridge network using available technology. A cost-effective approach to monitoring railway bridges is drive-by monitoring. Despite the potential of this approach, field tests and comprehensive experiments in the context of railway bridges are rare. There is a need for a robust methodology for frequency identification of railway bridges using drive-by monitoring, especially at regular operating speeds. The development and validation of a suitable methodology form the second focus of this work. In this dissertation, three experimental investigations were conducted to explore both the direct and indirect monitoring methodologies. The HUMVIB bridge, the railway bridge over the Schmutter river with an instrumented ICE 4, and a railway bridge in Düsseldorf (Germany) with an instrumented ICE TD were thoroughly analysed. During the investigations, modal expansion, a method that uses the structure's eigenmodes to reconstruct unmeasured structural responses, was confirmed as a suitable methodology for virtual sensing. The investigations under different operational and environmental conditions showed that the influence of operational conditions, such as train type and speed, is dominant over environmental conditions. The experiments also validated the developed methodology for frequency identification using drive-by monitoring for two different trains and bridges. This work aims to make contributions to the monitoring of railway bridges by providing practical, cost-effective, and reliable methods. |
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Uncontrolled Keywords: | Railway Bridges, Bridge Engineering, Structural Health Monitoring (SHM), Virtual Sensing, Drive-by Monitoring, Indirect Monitoring, Field Testing | ||||
Status: | Publisher's Version | ||||
URN: | urn:nbn:de:tuda-tuprints-264262 | ||||
Classification DDC: | 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering | ||||
Divisions: | 13 Department of Civil and Environmental Engineering Sciences > Institute für Structural Mechanics and Design 13 Department of Civil and Environmental Engineering Sciences > Institute für Structural Mechanics and Design > Structural Engineering and Dynamics of Structures (2024 renamed "Data-infused Modeling in Structural Dynamics") |
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TU-Projects: | VDI|19F2123A|ZEKISS | ||||
Date Deposited: | 14 Dec 2023 13:42 | ||||
Last Modified: | 22 Dec 2023 07:41 | ||||
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/26426 | ||||
PPN: | 514118490 | ||||
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